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Video logo removal detection based on sparse representation

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Abstract

With the popularity of multimedia editing tools, more and more forged multimedia content appeared on the network. Thus, the legal authorities need novel techniques to distinguish copyright infringements from a large number of videos on the Internet. Since logo removal is a common editing operation during unauthorized reproduction, logo removal detection is often equivalent to copyright infringements detection to some extent. In this paper, we proposed a video forensics framework for logo removal detection. Our framework mainly contains two stages: the removal traces detection and the removal region location. In the first stage, we use sparse representation to show the difference between the tampered areas and the original areas in sparsity. In the second stage, spatial priors and temporal correlations are used to refine the location of the removal regions. Finally, a spatiotemporal suspected region can obviously show the edited regions. The proposed method is validated on our video logo removal dataset by extensive experiments, showing promising results.

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Notes

  1. http://www.infognition.com/VirtualDubFilters/subtitles.html

  2. http://voidon.republika.pl/virtualdub/ladocs301/logoaway.html

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61572356 and 61303208) and the Tianjin Research Program of Application Foundation and Advanced Technology (15JCQNJC41600) and a grant from the China Scholarship Council (201706250187).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Xiao Jin, Yuting Su & Peiguang Jing

  2. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada

    Xiao Jin & Liang Zou

  3. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, China

    Chengqian Zhang

  4. School of Computer Science and Technology, Shandong University, Jinan, China

    Xuemeng Song

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  1. Xiao Jin
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  2. Yuting Su
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  3. Liang Zou
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  4. Chengqian Zhang
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  5. Peiguang Jing
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Correspondence to Yuting Su.

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Jin, X., Su, Y., Zou, L. et al. Video logo removal detection based on sparse representation. Multimed Tools Appl 77, 29303–29322 (2018). https://doi.org/10.1007/s11042-018-5959-8

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  • DOI: https://doi.org/10.1007/s11042-018-5959-8

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